Database metadata in immutable storage

ABSTRACT

A method for a database system includes storing table data for a database, the table data including information in rows and columns of one or more database tables. The method includes storing metadata on immutable storage, the metadata including information about the table data for the database. In one embodiment, mutable metadata may be periodically consolidated in the background to create new versions of metadata files and which allows for deletions of old metadata files and old data files.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of, and hereby claims priorityunder 35 U.S.C. § 120 to U.S. Non-Provisional patent application Ser.16/710,414, entitled “DATABASE METADATA IN IMMUTABLE STORAGE,” filed onDec. 11, 2019, which is a continuation of U.S. Non-Provisional patentapplication Ser. No. 15/812,892, entitled “DATABASE METADATA INIMMUTABLE STORAGE,” filed on Nov. 14, 2017.

TECHNICAL FIELD

The present disclosure relates systems, methods, and devices fordatabases and more particularly relates to storing and maintainingmetadata using non-mutable storage services.

BACKGROUND

Databases are widely used for data storage and access in computingapplications. Databases may include one or more tables that include orreference data that can be read, modified, or deleted using queries.Databases can store small or extremely large sets of data within one ormore tables. This data can be accessed by various users in anorganization or even be used to service public users, such as via awebsite or an application program interface (API). Both computing andstorage resources, as well as their underlying architecture, can play asignificant role in achieving desirable database performance.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present disclosureare described with reference to the following figures, wherein likereference numerals refer to like parts throughout the various figuresunless otherwise specified.

FIG. 1 is a block diagram illustrating a table having data stored infiles and associated metadata according to an example embodiment of thesystems and methods described herein.

FIG. 2 is a block diagram illustrating the table and metadata of FIG. 1after an addition to the table according to an example embodiment of thesystems and methods described herein.

FIG. 3 is a block diagram illustrating the table and metadata of FIG. 2after an addition to and deletion from the table according to an exampleembodiment of the systems and methods described herein.

FIG. 4 is a block diagram illustrating a processing platform for adatabase system according to an example embodiment of the systems andmethods described herein.

FIG. 5 is a block diagram illustrating components of a database servicemanager, according to one embodiment.

FIG. 6 is a block diagram illustrating a table having data stored infiles and associated metadata files in immutable storage according to anexample embodiment of the systems and methods described herein.

FIG. 7 is a block diagram illustrating the table and metadata of FIG. 6after changes to the table according to an example embodiment of thesystems and methods described herein.

FIG. 8 is a block diagram illustrating consolidation of metadata filesaccording to an example embodiment of the systems and methods describedherein.

FIG. 9 is a block diagram illustrating components of a configuration andmetadata manager, according to one embodiment.

FIG. 10 is a schematic flow chart diagram illustrating a method formanaging metadata in a database system, according to one embodiment.

FIG. 11 is a schematic flow chart diagram illustrating a method forcomputing a scan set, according to one embodiment.

FIG. 12 is a block diagram depicting an example computing deviceconsistent with at least one embodiment of processes and systemsdisclosed herein.

DETAILED DESCRIPTION

The present disclosure is directed to system, methods, and devices forstoring and maintaining mutable metadata using non-mutable storageservices, such as cloud storage resources. Database systems store andmaintain large amounts of metadata. This metadata describes the datathat is stored in database tables of customers, but is not actually thestored table data. Metadata can get very large, especially if there arelarge database tables of many customers. Current database systems havesevere limitations handling large amounts of metadata.

Current database systems store metadata in mutable storage devices andservices, including main memory, file systems, and key-value stores.These devices and services allow the metadata to be updated datain-place. If a data record changes, it may be updated with the newinformation. The old information is overwritten. This allows databasesto easily maintain mutable metadata: by updating metadata in-place.

However, these storage devices and services have limitations. Thelimitations are at least two-fold. First, mutable storage devices likemain memory and file systems have a hard limit in terms of storagecapacity. If the size of the metadata exceeds these limits, it isimpossible to store more metadata there. Second, mutable storageservices like key-value stores perform poorly when reading large volumesof metadata. Reading data is performed using range scans, which take along time to finish. In practice, range scans can take many minutes oreven approaching an hour to complete in large scale deployments.

These limitations make it impossible to store large amounts of metadataon existing mutable storage devices and services. Applicants havedeveloped systems and methods for improved metadata storage andmanagement that include storing metadata in immutable (non-mutable)storage. According to one embodiment, a method for storing or managing adatabase includes storing table data for a database. The table dataincludes information in rows and columns of one or more database tables.The method includes storing metadata on immutable storage. The metadataincludes information about the table data for the database, but may notinclude the table data.

As used herein, immutable or non-mutable storage includes storage wheredata cannot, or is not permitted to be overwritten or updated in-place.For example, changes to data that is located in a cell or region ofstorage media may be stored as a new file in a different, time-stamped,cell or region of the storage media. Mutable storage may include storagewhere data is or permitted to be overwritten or updated in place. Forexample, data in a given cell or region of the storage media can beoverwritten when there are changes to the data relevant to that cell orregion of the storage media.

In one embodiment, metadata is stored and maintained on non-mutablestorage services in the cloud. These storage services may include, forexample, Amazon S3®, Microsoft Azure Blob Storage®, and Google CloudStorage®. Many of these services do not allow to update data in-place(i.e., are non-mutable or immutable). Data files may only be added ordeleted, but never updated. In one embodiment, storing and maintainingmetadata on these services requires that, for every change in metadata,a metadata file is added to the storage service. These metadata filesmay be periodically consolidated into larger “compacted” or consolidatedmetadata files in the background. A metadata file version may be storedto indicate metadata files that correspond to the compacted orconsolidated version versus the pre-compaction or pre-consolidationversion of metadata files. In one embodiment, consolidation of mutablemetadata in the background to create new versions of metadata files mayallow for deletions of old metadata files and old data files.

By using immutable storage, such as cloud storage, embodiments allowstorage capacity to not have a hard limit. Using storage services in thecloud allows for virtually unlimited amounts of metadata. Reading largeamounts of metadata may be much faster because metadata files may bedownloaded in parallel, including prefetching of files. Metadata filesmay also be cached on a local file system so that they are notdownloaded more than once. In practical usage scenarios and testing,Applicants have seen a 200-fold performance improvement when readingmetadata from storage services in the cloud when compared to reading thesame metadata information from mutable storage like a key-value store.

A detailed description of systems and methods consistent withembodiments of the present disclosure is provided below. While severalembodiments are described, it should be understood that this disclosureis not limited to any one embodiment, but instead encompasses numerousalternatives, modifications, and equivalents. In addition, whilenumerous specific details are set forth in the following description toprovide a thorough understanding of the embodiments disclosed herein,some embodiments may be practiced without some or all these details.Moreover, for the purpose of clarity, certain technical material that isknown in the related art has not been described in detail to avoidunnecessarily obscuring the disclosure.

FIGS. 1-3 illustrate example operation of a database system when tabledata is stored in immutable storage (such as a cloud resource) andmetadata is stored in mutable storage (such as a local key-value store).FIGS. 6-8 illustrate example operation of a database system when bothtable data and metadata is stored in immutable storage. In one exampleembodiment, data in database tables is stored in files in the cloud.Metadata around tables and files is stored in the metadata store. Themetadata store may be a key-value store. Other example systems may useother technologies such as main memory storage or file system storage tostore metadata.

FIG. 1 illustrates a table 102 having data stored in files andassociated metadata. The table 102 is a “users” table stored in twophysical files F1 and F2 in cloud storage 104. The table 102 includes a“uid” column and a “name” column. The files F1 F2 include the data(e.g., the field values) for the rows and columns of the table 102.Specifically, file F1 includes the table data for the first three rows(i.e., uids 1, 2, and 3 and names Allison, Max, and Benoit) while fileF2 includes the table data for the last three rows (uids 4, 5, and 6,and names Neda, Thierry, and Florian). In one embodiment, each file F1and F2 stores data in a column-by-column format with the values for the“uid” column in a contiguous block and the values for the “name” columnin a contiguous block within the respective file.

File metadata is stored within metadata storage 106. The file metadatacontains table versions and information about each table data file, thiscase F1 and F2. The metadata storage 106 may include mutable storage(storage that can be over written or written in-place), such as a localfile system, system, memory, or the like.

In one embodiment, the file metadata consists of two data sets: tableversions and file information. The table versions data set includes amapping of table versions to lists of added files and removed files.File information consists of information about each file, including filepath, file size, file key id, and summaries of all rows and columns thatare stored in the file, for example. In the state illustrated, tableversion V1 indicates that files F1 and F2 were added (V1 ->added: F1,F2). The file information shows information about F1 (F1->“cloud://path/to/file1”, fileSize: 16 MB, fileKeyId: 3452, summariesof rows and columns, etc.) and F2 (F2 ->“/path/to/file2”, fileSize: 11MB, fileKeyId: 7965, summaries of rows and columns, etc.).

Each modification of the table creates new files and new file metadata.Inserts into the table create new files. Deletes from the table removefiles and potentially add new files with the remaining rows in a tableif not all rows in a file were deleted. Updates remove files and replacethem with new files with rows containing the updated records.

FIG. 2 illustrates the table and metadata of FIG. 1 after inserting arecord in the “users” table 102. By way of example, when inserting therecord (7, “Difei”) into table “users,” the data warehouse creates a newfile F3 in the cloud storage 104 that contains this record. Furthermore,the file metadata in the metadata storage 106 has been updated toinclude a new table version V2 and information about F3. Table versionV2 records that file F3 was added. File information includes the filepath, account, created timestamp, file size, and summaries of all rowsand columns that are stored file F3.

FIG. 3 illustrates the table and metadata of FIG. 2 after deleting arecord in the “users” table 102. For example, when deleting the record(4, “Neda”) from table “users,” the warehouse may create a new file F4that contains only two records (5, “Thierry”) and (6, “Florian”). FileF2 may be deleted from the cloud. File F4 may be the same as previousfile F2 except that row with uid “4” has been removed. The new file F4is stored in the cloud and the file metadata is updated with a new tableversion V3 and file information about file F4. V3 indicates that file F4has been added and that file F2 has been deleted.

When retrieving data from a table, the data warehouse may compute a scanset of all files that need to be read. The scan set is an aggregation ofall added files except files that were removed. The scan set may becomputed using table versions. When selecting data from the table at thecurrent time, the scan set is computed using all table versions up tothe latest table version. When selecting data from the table at anearlier time, the scan set is computed using all table versions up tothe table version that was current at the specified time. This techniqueof computing a scan set for any given time may be referenced herein as“time travel”. For example, when a user (e.g., User 1 404 in FIG. 4)selects data from table “users” in FIG. 3 after V3 has been implemented,a database service manager (e.g., database service manager 404 of FIG.4) computes the scan set using table versions V1, V2, V3. The scan setis an aggregation of all added files F1, F2, F3, F4 except deleted fileF2. Therefore, the scan set at the current time consists of files F1,F3, F4.

As another example, when selecting data at an earlier time when tableversion V2 was current, the scan set is computed using table versions V1and V2. The scan set is aggregation of all added files F1, F2, F3. Sincethere were no removed files, the scan set consists of files F1, F2, F3.In one embodiment, the scan set may be pruned using file information.For example, summaries of rows and columns of files may be used to prunetiles from the scan set because the contents of these files will not beneeded to compute a query result.

The above example method of storing file metadata in the metadatastorage 106 has limitations. It consumes too much space and results inslow performance. In practice, file metadata of hundreds of millions offiles results in terabytes of file metadata. This results in slowperformance when computing the scan set and pruning the scan set.Embodiments disclosed herein overcome one or more of these limitations.Storing and maintaining this (mutable) metadata on (non-mutable) cloudstorage allows a database system to have virtually unlimited storagecapacity and faster retrieval of metadata.

In one embodiment, metadata may be stored in metadata files in immutablestorage. In one embodiment, a system may write metadata files to cloudstorage for every modification of a database table. In one embodiment, asystem may download and read metadata files to compute the scan set. Themetadata files may be downloaded in parallel to improve scan setcomputation. In one embodiment, a system may periodically consolidatemetadata files in the background. In one embodiment, performanceimprovements, including pre-fetching, caching, columnar layout and thelike may be included. Furthermore, security improvements, includingencryption and integrity checking, are also possible with metadata fileswith a columnar layout.

Turning to FIG. 4, a block diagram is shown illustrating a processingplatform 400 for providing database services, according to oneembodiment. The processing platform 400 includes a database servicemanager 402 that is accessible by multiple users 404, 406, and 408. Thedatabase service manager 402 may also be referred to herein as aresource manager or global services. In some implementations, databaseservice manager 402 can support any number of users desiring access todata or services of the processing platform 400. Users 404-408 mayinclude, for example, end users providing data storage and retrievalqueries and requests, system administrators managing the systems andmethods described herein, software applications that interact with adatabase, and other components/devices that interact with databaseservice manager 402.

The database service manager 402 may provide various services andfunctions that support the operation of the systems and componentswithin the processing platform 400. Database service manager 402 hasaccess to stored metadata associated with the data stored throughoutdata processing platform 400. The database service manager 402 may usethe metadata for optimizing user queries. In some embodiments, metadataincludes a summary of data stored in remote data storage systems as wellas data available from a local cache (e.g., a cache within one or moreof the clusters of the execution platform 412). Additionally, metadatamay include information regarding how data is organized in the remotedata storage systems and the local caches. Metadata allows systems andservices to determine whether a piece of data needs to be processedwithout loading or accessing the actual data from a storage device.

As part of the data processing platform 400, metadata may be collectedwhen changes are made to the data using a data manipulation language(DML), which changes may be made by way of any DML statement. Examplesof manipulating data may include, but are not limited to, selecting,updating, changing, merging, and inserting data into tables. As part ofthe processing platform 400, files may be created and the metadata maybe collected on a per file and a per column basis. This collection ofmetadata may be performed during data ingestion or the collection ofmetadata may be performed as a separate process after the data isingested or loaded. In an implementation, the metadata may include anumber of distinct values; a number of null values; and a minimum valueand a maximum value for each file. in an implementation, the metadatamay further include string length information and ranges of charactersin strings.

In one embodiment, at least a portion of the metadata is stored inimmutable storage. For example, the metadata may be stored on thestorage platform 414 along with table data. In one embodiment, the sameor separate cloud storage resources as that used for table data may beallocated and used for the metadata. In one embodiment, the metadata maybe stored in local immutable storage. In one embodiment, informationabout the metadata in immutable storage, or information about metadatafiles stored in immutable storage, is stored in mutable storage 410. Theinformation about metadata may be referenced for locating and accessingthe metadata stored in immutable storage. In one embodiment, systemswith metadata storage may be restructured such that the metadata storageis used instead to store information about metadata files located inimmutable storage.

Database service manager 402 is further in communication with anexecution platform 412, which provides computing resources that executevarious data storage and data retrieval operations. The executionplatform 412 may include one or more compute clusters. The executionplatform 412 is in communication with one or more data storage devices416, 418, and 420 that are part of a storage platform 414. Althoughthree data. storage devices 416, 418, and 420 are shown in FIG. 4, theexecution platform 412 is capable of communicating with any number ofdata storage devices. In some embodiments, data storage devices 416,418, and 420 are cloud-based storage devices located in one or moregeographic locations. For example, data storage devices 416, 418, and420 may be part of a public cloud infrastructure or a private cloudinfrastructure, or any other manner of distributed storage system. Datastorage devices 416, 418, and 420 may include hard disk drives (HDDs),solid state drives (SSDs), storage clusters, or any other data storagetechnology. Additionally, the storage platform 414 may include adistributed file system (such as Hadoop Distributed File Systems(HDFS)), object storage systems, and the like.

In some embodiments, the communication links between database servicemanager 402 and users 404-408, mutable storage 410 for information aboutmetadata files (i.e., metadata file metadata), and execution platform412 are implemented via one or more data communication networks and maybe assigned various tasks such that user requests can be optimized.Similarly, the communication links between execution platform 412 anddata storage devices 416-420 in storage platform 414 are implemented viaone or more data communication networks. These data communicationnetworks may utilize any communication protocol and any type ofcommunication medium. In some embodiments, the data communicationnetworks are a combination of two or more data communication networks(or sub-networks) coupled to one another. In alternate embodiments,these communication links are implemented using any type ofcommunication medium and any communication protocol.

The database service manager 402, mutable storage 410, executionplatform 412, and storage platform 414 are shown in FIG. 4 as individualcomponents. However, each of database service manager 402, mutablestorage 410, execution platform 412, and storage platform 414 may beimplemented as a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations) or may be combinedinto one or more systems. Additionally, each of the database servicemanager 402, mutable storage 410, the execution platform 412, and thestorage platform 414 may be scaled up or down (independently of oneanother) depending on changes to the requests received from users404-408 and the changing needs of the data processing platform 400.Thus, in the described embodiments, the data processing platform 400 isdynamic and supports regular changes to meet the current data processingneeds.

FIG. 5 illustrates a block diagram depicting components of databaseservice manager 402, according to one embodiment. The database servicemanager 402 includes an access manager 502 and a key manager 504 coupledto a data storage device 506. The access manager 502 handlesauthentication and authorization tasks for the systems described herein.The key manager 504 manages storage and authentication of keys used.during authentication and authorization tasks. A request processingservice 508 manages received data storage requests and data retrievalrequests. A management console service 510 supports access to varioussystems and processes by administrators and other system managers.

The database service manager 402 also includes an SQL compiler 512, anSQL optimizer 514 and an SQL executor 516. SQL compiler 512 parses SQLqueries and generates the execution code for the queries. SQL optimizer514 determines the best method to execute queries based on the data thatneeds to be processed. SQL executor 516 executes the query code forqueries received by database service manager 402. A query scheduler andcoordinator 518 sends received queries to the appropriate services orsystems for compilation, optimization, and dispatch to an executionplatform 512. A virtual warehouse manager 520 manages the operation ofmultiple virtual warehouses.

Additionally, the database service manager 402 includes a configurationand metadata manager 522, which manages the information related to thedata stored in the remote data storage devices and in the local caches.A monitor and workload analyzer 524 oversees the processes performed bythe database service manager 402 and manages the distribution of tasks(e.g., workload) across the virtual warehouses and execution nodes inthe execution platform 412. Configuration and metadata manager 522 andmonitor and workload analyzer 524 are coupled to a data storage device526. In one embodiment, the configuration and metadata manger 522collects, stores, and manages metadata in an immutable storage resource.In one embodiment, updates to metadata result in new files and are notupdated in place.

Metadata files, as discussed herein, may include files that containmetadata of modifications (e.g., each modification) to any databasetable in a data warehouse. A modification of a database table maygenerate one or more metadata files, often just a single metadata file.In one embodiment, metadata files contain the following information:information about a metadata file, including a version number; a list ofall added table data files; a list of deleted table data files; andinformation about each added table data file, including file path, filesize, file key id, as well as summaries of all rows and columns that arestored in the table data file.

In one embodiment, the contents of metadata files may vary over time. Ifformat or content of a metadata file changes, the version number of themetadata file may be incremented. In one embodiment, the metadata store(or other mutable data storage resource) only stores information aboutmetadata files (which are stored in immutable storage), not about tabledata files. In practice, information about metadata files stored in inthe metadata store (or other mutable storage) is very limited and maycontain data for thousands of metadata files. In one embodiment,information for up to 30,000 metadata files may be stored within ametadata file. This dramatically reduces the amount of storage needed inthe metadata store or other mutable storage.

In one embodiment, a system writes metadata files to cloud storage forevery modification of a database table (e.g., modification of table datafiles). In addition to adding and deleting files, every modification toa database table in the data warehouse also generates one or moremetadata files. Typically, a modification creates a single metadatafile. However, if the modification to the table is large (e.g., aninsert into a table that produces very many files), it may result in thecreation of multiple metadata files. Further operation of theconfiguration and metadata manager 522 will be discussed further inrelation to FIGS. 6-12.

The database service manager 402 also includes a transaction managementand access control module 528, which manages the various tasks and otheractivities associated with the processing of data storage requests anddata access requests. For example, the transaction management and accesscontrol module 528 provides consistent and synchronized access to databy multiple users or systems. Since multiple users/systems may accessthe same data simultaneously, changes to the data may be synchronized toensure that each user/system is working with the current version of thedata. Transaction management and access control module 528 providescontrol of various data processing activities at a single, centralizedlocation in database service manager 402.

FIG. 6 illustrates the table 602 of FIG. 1 with metadata files stored incloud storage. The user's table 602 is shown with table data stored intable data files F1 and F2 within cloud storage 604, similar to thestructure shown in FIG. 1. However, metadata about the table data filesis stored in metadata file MF1 in the cloud storage 604 as well.Metadata file MF1 contains a list of added files F1 and F2, includingall file information about these files. For example, the fileinformation that was previously in the key-value store in the embodimentof FIG. 1 is in the metadata file (e.g., MF1). At the point in timeillustrated in FIG. 6, there are no deleted files indicated in themetadata file MF1. The metadata storage 606 only stores table versionV1, which maps to metadata file MF1, and information about metadata fileMF1. The information about metadata file MF1 includes the file path ofMF1 and may include more information. Thus, both table data files andmetadata files are stored in cloud storage, while information aboutmetadata files is stored in metadata storage 606 or other local and/ormutable storage.

FIG. 7 illustrates the table and metadata of FIG. 6 after adding arecord (7, “Difei”) and deleting a record (4, “Neda”). The firstmodification (insert uid “7” and name “Difei”) stored file F3 andmetadata file MF2 in the cloud. MF2 lists added file F3, including allfile information about F3. The metadata storage 606 is updated withtable version V2, which maps to MF2, and information about MF2. Thesecond modification (delete uid “4” and name “Neda”) stored file F4 andmetadata file MF3 in the cloud storage 604. MF3 lists added table datafile F4, including all file information of F4, and also lists deletedtable data files of F2.

The storage of the metadata files MF1, MF2, and MF3 in cloud storage 604or immutable storage allows for increased metadata storage capacity. Forexample, all metadata about the table data files F1, F2, F3, and F4 isfound within the cloud storage 604 in the metadata files MF1, MF2, andMF3. Metadata about the metadata files MF1 (information about themetadata), which is much smaller in size, is stored in a key-valuestore, mutable storage, and/or local storage.

In one embodiment, a data warehouse computes a scan set of files thatmust be read to answer a query. The scan set is computed using tableversions. Given a set of table versions, the data warehouse readsinformation about the corresponding metadata files from the metadatastore. It then downloads the metadata files from the cloud and reads thelist of added and delete files. Using these lists, it computes the scanset. Using file information stored in metadata files (e.g. informationabout rows and columns), the scan set may be pruned.

For example, when selecting data from table “users” at the timeillustrated in FIG. 7, the scan set is computed using table versions V1,V2, and V3. The warehouse reads information about corresponding metadatafiles MF1, MF2, and MF3. It downloads these metadata files from thecloud. The files may be downloaded in parallel. In one embodiment, thedatabase service manager 402 can begin reading one of the files even ifthe others have not yet completely downloaded. From the aggregated listof added files F1, F2, F3, and F4 it removes deleted file F2. Theresulting scan set would therefore be F1, F3, and F4. These files (orsub-portions of them) may be retrieved by an execution node forexecuting the query.

In one embodiment, metadata files are periodically consolidated in thebackground. Consolidation, or “compaction,” of metadata files aggregatesall added files of all metadata files and removes all deleted files fromthat list. Consolidation creates one or more compacted metadata filesthat contain only the resulting added-files list, including all fileinformation of these files. The purpose of consolidation two-fold.First, many metadata files are compacted into a much smaller set ofmetadata files for faster downloading and reading. Second, files thatare not referenced anymore in the compacted metadata files can beremoved from the cloud once the old metadata files are removed.

Metadata file versions distinguish different sets of metadata files. Thecompacted files in one metadata file version are a consolidation of allmetadata. files of the previous metadata file version. New metadatafiles are always registered under the latest metadata file version. Oldmetadata files may be deleted from cloud storage after they have beenconsolidated. All files that are not referenced in compacted files maybe deleted once they are not referenced in any metadata file anymore.

FIG. 8 is a block diagram illustrating consolidation of the metadatafiles shown in FIG. 7. Specifically, metadata files MF1, MF2, and MF3,are shown consolidated into compacted metadata file MF4. Metadata fileMF4 only contains added files F1, F3, F4 because F2 was deleted in MF3.MF4 also contains all file information of F1, F3, and F4. In oneembodiment, metadata file version MF V3 is created and MF4 is registeredunder MF V3. A new metadata file MF5 is registered under the latestmetadata file version MF V3. MF5 corresponds to table version V4 (notshown in FIG. 7). Table version V3 may point to either MF1, MF2, and MF3of MF V1 or to MF4 of MF V3, as they will result in the exact same scanset. As is illustrated, creation of the consolidated metadata file MF4allows for one file to do what previously took three files. In oneembodiment, an indication of a metadata file version may be stored aftercompleting consolidation so that a version before the consolidation maystill be determined or accessed. All subsequent table data changes maybe reflected based on MF4 or later. Thus, MF1, MF2, and MF3 may bedeleted, if desired or if they represent versions which no longer needto be maintained (e.g., for purposes of a “time travel” feature).

Constructing the scan set for a table version uses only metadata filesof a single metadata file version. The metadata file version to use isthe largest metadata file version that is smaller or equal than thegiven table version. For example, constructing the scan set for tableversion V3 in FIG. 7 uses metadata file version V3 because it is thelargest metadata file version that is smaller or equal to V3. Given theexample in FIG. 7, Table 1 provides a list of metadata files that mustbe read when constructing the scan set for a given table version:

TABLE 1 Table Metadata File Version Version Metadata Files Scan Set V1MF V1 MF1 F1, F2 V2 MF V1 MF1, MF2 F1, F2, F3 V3 MF V3 MF4 F1, F3, F4 V4MF V3 MF4, MF5 F3, F4, F5

In one embodiment, consolidation of metadata files happens in thebackground process in the data warehouse without any impact on the userworkload. New metadata files may be added while compacted files arecomputed. Only when the compacted file has been uploaded to the cloud itmay be used to compute that scan set.

Various performance improvements may be achieved with the immutablestorage of metadata. In one embodiment, metadata files are prefeched.For example, when downloading a set metadata files, the data warehousedownloads the metadata files in parallel in the background before themetadata files are opened by the process. Pre-fetching improves readingtime of metadata files because when the process wants to open a metadatafile it may have already been downloaded using pre-fetching.

In one embodiment, metadata files are cached. Metadata files may becached on the local file system of a process. Metadata files may only bedownloaded once, even if they are read by many difference processes thatshare the same file system. Old cached metadata files may be deletedfrom the cache if the cache grows out of space. In this case, themetadata files may be downloaded again as needed.

In one embodiment, metadata files have a columnar layout. Fileinformation within metadata files is stored with a columnar layout. Thismeans the format of the metadata file is not row-by-row, butcolumn-by-column. If a process reads information about a column in ametadata file, it only needs to read a single, contiguous block ofbytes. In one embodiment, every block of bytes is compressed using astandard compression algorithm (“gzip”). Both these techniques improvedread performance.

Security improvements are also implemented in some embodiments. In oneembodiment, metadata files are encrypted using individual file keys.Within a metadata file, columns may be encrypted individually usingAES-CTR mode with different start counters. This allows a databasesystem to read an individual column from a metadata file because it canbe decrypted without needing to decrypt the whole file at once.Encryption improves security because nobody can read the metadata filewithout having the proper file key.

For verification that metadata files have not been altered, the systemmay store hashes of columns for each column within a metadata file.Before decrypting the data, the system compares the hash of theencrypted column with the stored hash of the column of this metadatafile. If the hashes do not match, the metadata file must have beenaltered. This improves security because altering of metadata files aredetected by the database system.

FIG. 9 is a schematic block diagram illustrating components of aconfiguration and metadata manager 522, according to one embodiment. Theconfiguration and metadata manager 522 may collect, store, and managemetadata about table data files as well as metadata about metadatafiles. The configuration and metadata manager 522 includes a table datacomponent 902, a metadata component 904, a metadata informationcomponent 906, a consolidation component 908, a scan set component 910,an encryption component 912, and a hash component 914. The components902-914 are given by way of illustration only and may not all beincluded in all embodiments. In fact, some embodiments may include onlyone or any combination of two or more of the components 902-914. Forexample, some of the components may be located outside or separate fromthe configuration and metadata manager 522, such as within a databaseservice manager 402 or processing platform 400. Furthermore, thecomponents 902-914 may comprise hardware, computer readableinstructions, or a combination of both to perform the functionality andprovide the structures discussed herein.

The table data component 902 stores table data for a database, the tabledata includes information in rows and columns of one or more databasetables. The table data component 902 may store table data in table datafiles within a storage resource. Example storage resources include cloudstorage and/or immutable storage. In one embodiment, the storageresources for storage of table data files may be dynamically allocatedto accommodate increases or decreases in storage requirement. The tabledata component 902 may manage and store table data by causing the datato be stored or updated in a remote resource, such as a cloud storageresource or service.

The metadata component 904 stores metadata on immutable storage. Themetadata may include information about or describing the table data forthe database stored by the table data component 902. In one embodiment,the metadata files may include metadata such as an indication of addedor deleted table data files. The metadata may include file informationfor table data files, the file information including one or more of afile name and a storage location. In one embodiment, the metadata may bestored in files on the same cloud storage resources as the table data.In one embodiment, metadata component 904 may cause the metadata to bestored within metadata files in a column-by-column format in remotecloud storage.

The metadata component 904 may also collect and manage storage ofmetadata within metadata files on the immutable storage. The metadatacomponent 904 may create, in response to a change in the table data, anew metadata file in the immutable storage without modifying previousmetadata files. The new metadata file may include metadata indicatingthe change in the table data. In one embodiment, the metadata in the newmetadata file indicates an addition or a deletion of a table data filecomprising the table data. The metadata component 904 may also deleteexpired metadata files. Expired metadata files may include those olderthan a specific age and that are not referenced in metadata informationstored by the metadata information component 906.

The metadata information component 906 stores and manages informationabout the metadata in mutable storage. The information about themetadata (metadata about metadata files) may be stored in local mutablestorage and/or in metadata storage (or what was previously referenced asmetadata storage. In one embodiment, however, the information about themetadata only includes information about metadata files, not metadataabout table data files. Thus, all table data metadata may be located inimmutable storage. In one embodiment, the information about metadata maybe stored and updated in place. For example, the information about themetadata, in one embodiment, is stored in a key-value store. Theinformation about the metadata includes information indicating a versionand indicating one or more metadata files that included metadatacorresponding to the version.

The consolidation component 908 consolidates or compacts metadata fromtwo or more old metadata files into a consolidated metadata file. In oneembodiment, the consolidated metadata file includes metadata reflectingthe table data changes indicated in the two or more old metadata files.In one embodiment, the consolidation component 908 deletes the two ormore old metadata files. The consolidation component 908 may delete oneor more table data files not referenced by metadata in the consolidatedmetadata file.

The scan set component 910 is may compute a scan set for a query. In oneembodiment, a database system may receive a query directed to a databasethat includes the table data. The scan set component may retrieve aplurality of uncached metadata files, or cause another component to doso. The metadata files may include metadata files that correspond to thequery. In one embodiment, the scan set component downloads the metadatafiles in parallel from the immutable storage. In one embodiment, thescan set component determines the scan set by reading a first metadatafile before a second metadata file has been fully downloaded. This mayallow for improved speed in computing scan sets because the processingand downloading of metadata can be done tile by file or in chunks. Thus,a database system does not need to wait for all files to download beforeit starts computing the scan set, it can compute the scan set as themetadata files are retrieved (either from cache or from immutablestorage). In one embodiment, the scan set indicates one or more tabledata files needed to perform the query.

The encryption component 912 is configured to encrypt table data andmetadata. In one embodiment, the encryption component 912 encrypts themetadata column-by-column to allow for independent decryption andreading of metadata for a specific column.

The hash component 914 computes and stores hashes for columns. Forexample, upon creating a metadata file, the hash component 814 maycompute a has for each column in the metadata file and store the hash.Later, when a column in the file is accessed, the hash component 914 maycompute the hash and compare it to the stored hash. If the hashes aredifferent, the hash component 914 may determine that the metadata inthat column has been altered.

FIG. 10 is a schematic flow chart diagram illustrating an example method1000 for managing metadata in a database system. The method 1000 may beperformed by a configuration and metadata manager 522, database servicemanager 402, processing platform 400, and/or other service or platform.

The method 1000 begins and a table data component 902 stores 1002 tabledata for a database, the table data including information in rows andcolumns of one or more database tables. A metadata component 904 stores1004 metadata on immutable storage, the metadata includes informationabout the table data for the database. The metadata component 904creates 1006, in response to a change in the table data, a new metadatafile in the immutable storage without modifying previous metadata files.The new metadata file includes metadata indicating the change in thetable data. A consolidation component 908 consolidates 1008 metadatafrom two or more old metadata files into a consolidated metadata file.

FIG. 11 is a schematic flow chart diagram illustrating an examplemethod. 1000 for computing a scan set in a database system. The method1100 may be performed by a configuration and metadata manager 522,database service manager 402, processing platform 400, and/or otherservice or platform.

The method 1100 begins and a database system receives 1102 a querydirected to a database comprising the table data. A scan set component910 identifies 1104 one or more relevant metadata files. For example,the scan set component 910 may identify 1104 the relevant metadata filesbased on information about the metadata files stored in metadata storageor immutable storage. The scan set component 910 retrieves 1106 aplurality of uncached metadata files corresponding to the query inparallel from the immutable storage. For example, if there are aplurality of metadata files that are needed to compute the scan set, butare not located in cache, the plurality of metadata files may bedownloaded in parallel. The scan set component 910 reads 1108 a firstmetadata file before a second metadata file has been fully downloaded todetermine the scan set. For example, the scan set component 910 does notneed to wait until all metadata files are downloaded to begin computethe scan set, it can begin computing the scan set as files areretrieved/downloaded. The scan set may be provided to an execution nodeand/or otherwise used to retrieve table data files or information neededfor processing a query.

FIG. 12 is a block diagram depicting an example computing device 1200.In some embodiments, computing device 1200 is used to implement one ormore of the systems and components discussed herein. For example,computing device 1200 may include or be part of a configuration andmetadata manager 522, a database service manager 402, a processingplatform 400, and/or any other components or systems discussed herein.As another example, the components, systems, or platforms discussedherein may include one or more computing devices 1200. Further,computing device 1200 may interact with any of the systems andcomponents described herein. Accordingly, computing device 1200 may beused to perform various procedures and tasks, such as those discussedherein. Computing device 1200 can function as a server, a client or anyother computing entity. Computing device 1200 can be any of a widevariety of computing devices, such as a desktop computer, a notebookcomputer, a server computer, a handheld computer, a tablet, and thelike.

Computing device 1200 includes one or more processor(s) 1202, one ormore memory device(s) 1204, one or more interface(s) 1206, one or moremass storage device(s) 1208, and one or more Input/Output (I/O)device(s) 1210, all of which are coupled to a bus 1212. Processor(s)1202 include one or more processors or controllers that executeinstructions stored in memory device(s) 1204 and/or mass storagedevice(s) 1208. Processor(s) 1202 may also include various types ofcomputer-readable media, such as cache memory.

Memory device(s) 1204 include various computer-readable media, such asvolatile memory (e.g., random access memory (RAM)) and/or nonvolatilememory (e.g., read-only memory (ROM)). Memory device(s) 1204 may alsoinclude rewritable ROM, such as Flash memory.

Mass storage device(s) 1208 include various computer readable media,such as magnetic tapes, magnetic disks, optical disks, solid statememory (e.g., Flash memory), and so forth. Various drives may also beincluded in mass storage device(s) 1208 to enable reading from and/orwriting to the various computer readable media. Mass storage device(s)1208 include removable media and/or non-removable media.

I/O device(s) 1210 include various devices that allow data and/or otherinformation to be input to or retrieved from computing device 1200.Example I/O device(s) 1210 include cursor control devices, keyboards,keypads, microphones, monitors or other display devices, speakers,printers, network interface cards, modems, lenses, CCDs or other imagecapture devices, and the like.

Interface(s) 1206 include various interfaces that allow computing device1200 to interact with other systems, devices, or computing environments.Example interface(s) 1206 include any number of different networkinterfaces, such as interfaces to local area networks (LANs), wide areanetworks (WANs), wireless networks, and the Internet.

Bus 1212 allows processor(s) 1202, memory device(s) 1204, interface(s)1206, mass storage device(s) 1208, and I/O device(s) 1210 to communicatewith one another, as well as other devices or components coupled to bus1212. Bus 1212 represents one or more of several types of busstructures, such as a system bus, PCI bus, IEEE 1394 bus, USB bus, andso forth.

Examples

The following examples pertain to further embodiments.

Example 1 is a method that includes storing table data for a database,the table data including information in rows and columns of one or moredatabase tables. The method includes storing metadata on immutablestorage, the metadata including information about the table data for thedatabase.

In Example 2, storing the metadata as in Example 1 includes storing andmanaging metadata in files on the immutable storage.

In Example 3, the metadata as in any of Examples 1-2 includes anindication of added or deleted table data files.

in Example 4, the metadata as in any of Examples 1-3 includes fileinformation for files including the table data, the file informationincluding one or more of a file name and a storage location.

In Example 5, the immutable storage as in any of Examples 1-4 includes acloud storage resource.

in Example 6, the method as in any of Examples 1-5 includes storinginformation about the metadata in mutable storage.

In Example 7, the information about the metadata as of Example 6 isstored in a key-value store.

In Example 8, the information about the metadata as in any of Examples6-7 includes information indicating a version and indicating one or moremetadata files including metadata corresponding to the version.

In Example 9, the method as in any of Examples 1-8 further includes, inresponse to a change in the table data, creating a new metadata file inthe immutable storage without modifying previous metadata files, the newmetadata file including metadata indicating the change in the tabledata.

In Example 10, the metadata in the new metadata file of Example 9indicates an addition or a deletion of a table data file including thetable data.

In Example 11, the method as in any of Examples 9-10 further includesdeleting an expired metadata file.

In Example 12, the method as in any of Examples 1-11 further includesconsolidating metadata from two or more old metadata files into aconsolidated metadata file.

in Example 13, the consolidated metadata file of Example 12 includesmetadata reflecting the table data changes indicated in the two or moreold metadata files.

In Example 14, the consolidating in any of Examples 12-13 includesdeleting the two or more old metadata files.

In Example 15, the method as in any of Examples 12-14 further includesdeleting one or more table data files not referenced by metadata in theconsolidated metadata file.

In Example 16, the method as in any of Examples 1-15 further includesreceiving a query directed to a database including the table data and.retrieving a plurality of uncached metadata files corresponding to thequery in parallel from the immutable storage.

In Example 17, the method of Example 16 further includes determining ascan set of table data files based on metadata files corresponding tothe query, wherein determining the scan set includes reading a firstmetadata file before a second metadata file has been fully downloaded,the plurality of uncached metadata files including the first metadatafile and the second metadata file.

In Example 18, the scan set of Example 17 indicates one or more tabledata files needed to perform the query.

In Example 19, storing the metadata as in any of Examples 1-18 includesstoring the metadata in a column-by-column format.

In Example 20, the method of Example 19 further includes encrypting themetadata column-by-column to allow for independent decryption andreading of metadata for a specific column.

In Example 21, the method of any of Examples 19-20 further includesstoring a hash of a column of metadata and comparing the stored hashwith a computed hash (recently computed hash) to determine whether themetadata has been altered.

Example 22 is an apparatus including means to perform a method as in anyof Examples 1-21.

Example 23 is a machine-readable storage including machine-readableinstructions that, when executed, implement a method or realize anapparatus of any of Examples 1-22.

The flow diagrams and block diagrams herein illustrate the architecture,functionality, and operation of possible implementations of systems,methods, and computer program products according to various embodimentsof the present disclosure. In this regard, each block in the flowdiagrams or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It will also be notedthat each block of the block diagrams and/or flow diagrams, andcombinations of blocks in the block diagrams and/or flow diagrams, maybe implemented by special purpose hardware-based systems that performthe specified functions or acts, or combinations of special purposehardware and computer instructions. These computer program instructionsmay also be stored in a computer-readable medium that can direct acomputer or other programmable data processing apparatus to function ina particular manner, such that the instructions stored in thecomputer-readable medium produce an article of manufacture includinginstruction means which implement the function/act specified in the flowdiagram and/or block diagram block or blocks.

The systems and methods described herein provide a flexible and scalabledata warehouse using new data processing platforms, methods, systems,and algorithms. In some embodiments, the described systems and methodsleverage a cloud infrastructure that supports cloud-based storageresources, computing resources, and the like. Example cloud-basedstorage resources offer significant storage capacity available on-demandat a low cost. Further, these cloud-based storage resources may befault-tolerant and highly scalable, which can be costly to achieve inprivate data storage systems. Example cloud-based computing resourcesare available on-demand and may be priced based on actual usage levelsof the resources. Typically, the cloud infrastructure is dynamicallydeployed, reconfigured, and decommissioned in a rapid manner.

In the described systems and methods, a data storage system utilizes anSQL (Structured Query Language)-based relational database. However,these systems and. methods are applicable to any type of database usingany data storage architecture and using any language to store andretrieve data within the database. The systems and methods describedherein may also provide a multi-tenant system that supports isolation ofcomputing resources and data between different customers/clients andbetween different users within the same customer/client.

Various techniques, or certain aspects or portions thereof, may take theform of program code (i.e., instructions) embodied in tangible media,such as floppy diskettes, CD-ROMs, hard drives, a non-transitorycomputer readable storage medium, or any other machine readable storagemedium wherein, when the program code is loaded into and executed by amachine, such as a computer, the machine becomes an apparatus forpracticing the various techniques. In the case of program code executionon programmable computers, the computing device may include a processor,a storage medium readable by the processor (including volatile andnon-volatile memory and/or storage elements), at least one input device,and at least one output device. The volatile and non-volatile memoryand/or storage elements may be a RAM, an EPROM, a flash drive, anoptical drive, a magnetic hard drive, or another medium for storingelectronic data. One or more programs that may implement or utilize thevarious techniques described herein may use an application programminginterface (API), reusable controls, and the like. Such programs may beimplemented in a high-level procedural or an object-oriented programminglanguage to communicate with a computer system. However, the program(s)may be implemented in assembly or machine language, if desired. In anycase, the language may be a compiled or interpreted language, andcombined with hardware implementations.

It should be understood that many of the functional units described inthis specification may be implemented as one or more components, whichis a teen used to more particularly emphasize their implementationindependence. For example, a component may be implemented as a hardwarecircuit comprising custom very large scale integration (VLSI) circuitsor gate arrays, off-the-shelf semiconductors such as logic chips,transistors, or other discrete components. A component may also beimplemented in programmable hardware devices such as field programmablegate arrays, programmable array logic, programmable logic devices, orthe like.

Components may also be implemented in software for execution by varioustypes of processors. An identified component of executable code may, forinstance, comprise one or more physical or logical blocks of computerinstructions, which may, for instance, be organized as an object, aprocedure, or a function. Nevertheless, the executables of an identifiedcomponent need not be physically located together, but may comprisedisparate instructions stored in different locations that, when joinedlogically together, comprise the component and achieve the statedpurpose for the component.

Indeed, a component of executable code may be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within components, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork. The components may be passive or active, including agentsoperable to perform desired functions.

Reference throughout this specification to “an example” means that aparticular feature, structure, or characteristic described in connectionwith the example is included in at least one embodiment of the presentdisclosure. Thus, appearances of the phrase “in an example” in variousplaces throughout this specification are not necessarily all referringto the same embodiment.

As used herein, a plurality of items, structural elements, compositionalelements, and/or materials may be presented in a common list forconvenience. However, these lists should be construed as though eachmember of the list is individually identified as a separate and uniquemember. Thus, no individual member of such list should be construed as ade facto equivalent of any other member of the same list solely based onits presentation in a common group without indications to the contrary.In addition, various embodiments and examples of the present disclosuremay be referred to herein along with alternatives for the variouscomponents thereof. It is understood that such embodiments, examples,and alternatives are not to be construed as de facto equivalents of oneanother, but are to be considered as separate and autonomousrepresentations of the present disclosure.

Although the foregoing has been described in some detail for purposes ofclarity, it will be apparent that certain changes and modifications maybe made without departing from the principles thereof. It should benoted that there are many alternative ways of implementing both theprocesses and apparatuses described herein. Accordingly, the presentembodiments are to be considered illustrative and not restrictive.

Those having skill in the art will appreciate that many changes may bemade to the details of the above-described embodiments without departingfrom the underlying principles of the disclosure. The scope of thepresent disclosure should, therefore, be determined only by thefollowing claims.

What is claimed is:
 1. A method comprising: generating and storing anew-version set of one or more table-metadata files, the new-version setof one or more table-metadata files comprising table metadata for a newversion of a database table; caching, in connection with processing afirst query, one or more of table-metadata files in at least one of acurrent-version set of one or more table-metadata files and a newversion set of one or more table-metadata files; receiving a subsequentquery directed to the database table; downloading, in connection withprocessing the subsequent query, at least one uncached table-metadatafile in a scan set of table-metadata files for the subsequent query; andprocessing the subsequent query using the at least one uncachedtable-metadata file.
 2. The method of claim 1, further comprising:determining that a plurality of table-metadata files are not included ina cache; downloading, in parallel, the plurality of table-metadata filesfrom immutable storage; and storing, in the cache, the plurality oftable-metadata files.
 3. The method of claim 2, wherein the immutablestorage is provided by a cloud storage resource.
 4. The method of claim2, further comprising: reading, among the plurality of table-metadatafiles, a first table-metadata file before a second table-metadata filehas been fully downloaded, the plurality of table-metadata filescomprising at least the first table-metadata file and the secondtable-metadata file.
 5. The method of claim 4, wherein reading the firsttable-metadata file occurs during a process for generating the scan setof table-metadata files.
 6. The method of claim 1, wherein the scan setof table-metadata files indicates at least one table-metadata file forperforming the subsequent query.
 7. The method of claim 1, wherein theone or more of table-metadata files are cached in a local cache withinat least one cluster of an execution platform.
 8. The method of claim 1,further comprising: determining first version number of a currentversion set of one or more table-data files determining a second versionnumber of a particular version set of the table-metadata files that issmaller or equal to than the first version number; and generating thescan set of the table-metadata files based on the particular version setof the table-metadata files.
 9. The method of claim 8, wherein thesecond version number comprises a smaller number than the first versionnumber.
 10. The method of claim 9, wherein the smaller number indicatesthat at least a portion of one new table-metadata file has yet to bestored in immutable storage.
 11. A system comprising: at least oneprocessor; and a memory device including instructions, which whenexecuted by the at least one processor, cause the at least one processorto perform operations comprising: generating and storing a new-versionset of one or more table-metadata files, the new-version set of one ormore table-metadata files comprising table metadata for a new version ofa database table; caching, in connection with processing a first query,one or more of table-metadata files in at least one of a current-versionset of one or more table-metadata files and a new version set of one ormore table-metadata files; receiving a subsequent query directed to thedatabase table; downloading, in connection with processing thesubsequent query, at least one uncached table-metadata file in a scanset of table-metadata files for the subsequent query; and processing thesubsequent query using the at least one uncached e-metadata file. 12.The system of claim 11, wherein the memory device includes furtherinstructions, which when executed by the at least one processor, causethe at least one processor to perform further operations comprising:determining that a plurality of table-metadata files are not included ina cache; downloading, in parallel, the plurality of table-metadata filesfrom immutable storage; and storing, in the cache, the plurality oftable-metadata files.
 13. The system of claim 12, wherein the immutablestorage is provided by a cloud storage resource.
 14. The system of claim12, wherein the memory device includes further instructions, which whenexecuted by the at least one processor, cause the at least one processorto perform further operations comprising: reading, among the pluralityof table-metadata files, a first table-metadata file before a secondtable-metadata file has been fully downloaded, the plurality oftable-metadata files comprising at least the first table-metadata fileand the second table-metadata file.
 15. The system of claim 14, whereinreading the first table-metadata file occurs during a process forgenerating the scan set of table-metadata files.
 16. The system of claim11, wherein the scan set of table-metadata files indicates at least onetable-metadata file for performing the subsequent query.
 17. The systemof claim 11, wherein the one or more of table-metadata files are cachedin a local cache within at least one cluster of an execution platform.18. The system of claim H, wherein the memory device includes furtherinstructions, which when executed by the at least one processor, causethe at least one processor to perform further operations comprising:determining first version number of a current version set of one or moretable-data files determining a second version number of a particularversion set of the table-metadata files that is smaller or equal to thanthe first version number; and generating the scan set of thetable-metadata files based on the particular version set of thetable-metadata files.
 19. The system of claim 18, wherein the secondversion number comprises a smaller number than the first version number.20. The system of claim 19, wherein the smaller number indicates that atleast a portion of one new table-metadata file has yet to be stored inimmutable storage.
 21. A non-transitory computer-readable mediumcomprising instructions, which when executed by at least one processor,cause the at least one processor to perform operations comprising:generating and storing a new-version set of one or more table-metadatafiles, the new-version set of one or more table-metadata filescomprising table metadata for a new version of a database table;caching, in connection with processing a first query, one or more oftable-metadata files in at least one of a current-version set of one ormore table-metadata files and a new version set of one or moretable-metadata files; receiving a subsequent query directed to thedatabase table; downloading, in connection with processing thesubsequent query, at least one uncached table-metadata file in a scanset of table-metadata files for the subsequent query; and processing thesubsequent query using the at least one uncached table-metadata file.22. The non-transitory computer-readable medium of claim 21, wherein thenon-transitory computer-readable medium comprises further instructions,which when executed by the at least one processor, further cause the atleast one processor to perform further operations comprising:determining that a plurality of table-metadata files are not included ina cache; downloading, in parallel, the plurality of table-metadata filesfrom immutable storage; and storing, in the cache, the plurality oftable-metadata files.
 23. The non-transitory computer-readable medium ofclaim 22, wherein the immutable storage is provided by a cloud storageresource.
 24. The non-transitory computer-readable medium of claim 22,wherein the non-transitory computer-readable medium comprises furtherinstructions, which when executed by the at least one processor, furthercause the at least one processor to perform further operationscomprising: reading, among the plurality of table-metadata files, afirst table-metadata file before a second table-metadata file has beenfully downloaded, the plurality of table-metadata files comprising atleast the first table-metadata file and the second table-metadata file.25. The non-transitory computer-readable medium of claim 24, whereinreading the first table-metadata file occurs during a process forgenerating the scan set of table-metadata files.
 26. The non-transitorycomputer-readable medium of claim 21, wherein the scan set oftable-metadata files indicates at least one table-metadata file forperforming the subsequent query.
 27. The non-transitorycomputer-readable medium of claim 21, wherein the one or more oftable-metadata files are cached in a local cache within at least onecluster of an execution platform.
 28. The non-transitorycomputer-readable medium of claim 21, wherein the non-transitorycomputer-readable medium comprises further instructions, which whenexecuted by the at least one processor, further cause the at least oneprocessor to perform further operations comprising: determining firstversion number of a current version set of one or more table-data filesdetermining a second version number of a particular version set of thetable-metadata files that is smaller or equal to than the first versionnumber; and generating the scan set of the table-metadata files based onthe particular version set of the table-metadata files.
 29. Thenon-transitory computer-readable medium of claim 28, wherein the secondversion number comprises a smaller number than the first version number.30. The non-transitory computer-readable medium of claim 29, wherein thesmaller number indicates that at least a portion of one newtable-metadata file has yet to be stored in immutable storage.